An Adaptive Method for Spiral Segmentation of Aztec Compact Code Images with Irregular Grid Structure

Reading Aztec codes is crucial in many practical applications and is well-studied for simple scenarios. However, mobile phone-based decoding is challenging under uncontrolled conditions and when the codes are printed on irregular surfaces like warped paper. The codes must remain readable, even though paper is flexible and not perfectly planar. Our novel method addresses this problem by considering local variations in adjacent symbol modules using conventional image processing techniques. It is particularly effective for Aztec Compact symbols lacking reference elements. We evaluate it on the specially modelled CoBRA-CYL-AZ dataset, including curved and cropped symbol examples, and further confirm the method's applicability on small dataset of the real photos. Both synthetic and real datasets are made publicly accessible on Zenodo. The proposed method achieves 0.59 accuracy on the CoBRA-CYL-AZ dataset, significantly outperforming the popular open-source readers: ZXing (0.02), ZXing-cpp (0.16), and Dynamsoft (0.16). While our method is applicable with any Aztec symbology, it features scanning distorted and damaged Aztec Compact codes.